





"""simple docstring"""





import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


SCREAMING_SNAKE_CASE__   =  {
    "n_samples": 64,
    "horizon": 32,
    "num_inference_steps": 20,
    "n_guide_steps": 2,  # can set to 0 for faster sampling, does not use value network
    "scale_grad_by_std": True,
    "scale": 0.1,
    "eta": 0.0,
    "t_grad_cutoff": 2,
    "device": "cpu",
}


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  "hopper-medium-v2"
       SCREAMING_SNAKE_CASE__   =  gym.make(env_name)

       SCREAMING_SNAKE_CASE__   =  ValueGuidedRLPipeline.from_pretrained(
           "bglick13/hopper-medium-v2-value-function-hor32",
           env=env,
       )

       env.seed(0)
       SCREAMING_SNAKE_CASE__   =  env.reset()
       SCREAMING_SNAKE_CASE__   =  0
       SCREAMING_SNAKE_CASE__   =  0
       SCREAMING_SNAKE_CASE__   =  1_000
       SCREAMING_SNAKE_CASE__   =  [obs.copy()]
       try:
              for t in tqdm.tqdm(range(T)):
                     # call the policy
                     SCREAMING_SNAKE_CASE__   =  pipeline(obs, planning_horizon=32)

                     # execute action in environment
                     SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__   =  env.step(denorm_actions)
                     SCREAMING_SNAKE_CASE__   =  env.get_normalized_score(total_reward)

                     # update return
                     total_reward += reward
                     total_score += score
                     print(
                         f'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
                         f' {total_score}'
                     )

                     # save observations for rendering
                     rollout.append(next_observation.copy())

                     SCREAMING_SNAKE_CASE__   =  next_observation
       except KeyboardInterrupt:
              pass

       print(f'Total reward: {total_reward}')













"""simple docstring"""





import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
       import torch

       from transformers import AutoModelForImageClassification

if is_vision_available():
       from transformers import AutoImageProcessor

@require_torch
@require_vision
class 						lowercase   (   unittest.TestCase   ):





      @slow
      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            lowerCAmelCase    		=			AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip"""					)
            model.to(lowercase					)

            from datasets import load_dataset

            lowerCAmelCase    		=			load_dataset("""nielsr/rvlcdip-demo"""					)

            lowerCAmelCase    		=			dataset["""train"""][0]["""image"""].convert("""RGB"""					)

            lowerCAmelCase    		=			image_processor(lowercase ,		return_tensors="""pt"""					).to(lowercase					)

            # forward pass
            with torch.no_grad():
                  lowerCAmelCase    		=			model(**lowercase					)
                  lowerCAmelCase    		=			outputs.logits

            lowerCAmelCase    		=			torch.Size((1, 16)					)
            self.assertEqual(logits.shape ,		lowercase					)

            lowerCAmelCase    		=			torch.tensor(
                [-0.4_158, -0.4_092, -0.4_347] ,		device=lowercase ,		dtype=torch.float ,		)
            self.assertTrue(torch.allclose(logits[0, :3] ,		lowercase ,		atol=1e-4					)					)






